Background: The incidence of endometrial cancer (EC) is rising globally across all age groups. Endometrial intraepithelial neoplasia (EIN) is a premalignant lesion that may progress to EC if untreated. A clinical model is needed to efficiently identify women requiring prompt evaluation while avoiding unnecessary invasive procedures. Obesity is a major risk factor, but whether Asian women require a lower body mass index (BMI) cutoff than the World Health Organization (WHO) definition remains debated. This study aimed to develop a multivariable risk prediction model to guide biopsy decisions and determine an appropriate BMI cutoff for predicting EIN/EC risk among Asian women. Methods: This study retrospectively reviewed 1192 women aged ≥18 years who underwent hysteroscopy between 2010 and 2023 at a tertiary hospital. Candidate predictors included patient age, parity, BMI, postmenopausal status, symptom of abnormal uterine bleeding (AUB), diabetes mellitus, hypertension, polycystic ovary syndrome (PCOS), use of oral contraceptives, intrauterine devices, or menopausal hormone therapy, tamoxifen treatment, presence of multiple polyps, and endometrial thickness (EMT) measured by transvaginal ultrasonography. Multivariable logistic regression with stepwise selection identified independent predictors, and model stability and calibration were assessed using 1000 bootstrap resamples. Results: EIN/EC was diagnosed in 55 patients (4.6%). Six independent predictors were identified: postmenopausal status (adjusted odds ratio [aOR] 5.93, 95% CI 2.92–12.04), AUB (aOR 4.07, 1.51–10.97), multiple polyps (aOR 2.49, 1.33–4.66), PCOS (aOR 2.37, 1.08–5.22), BMI (aOR 1.13 per kg/m2; 1.84 per +5 kg/m2), and EMT (aOR 1.07 per mm, 1.02–1.11). When using categorical cutoffs, Obese II (BMI ≥ 30 kg/m2) and markedly increased EMT (≥20 mm) remained significant. Predicted probabilities ranged from 0.3% with no risk factors to 90.9% with all six risk factors present. The final model demonstrated good discrimination (AUC 0.79, 95% CI 0.73–0.86) and excellent calibration on bootstrap validation (mean absolute error 0.005). Conclusions: This six-factor clinical model stratifies individual EIN/EC risk using readily available variables and may guide timely, risk-based biopsy decisions by identifying high-risk patients while minimizing unnecessary procedures in low-risk cases. BMI ≥ 30 kg/m2(WHO obesity threshold) was confirmed as a meaningful cutoff, but external validation is warranted to confirm its generalizability and clinical applicability.
背景：子宫内膜癌（EC）的发病率在全球各年龄段均呈上升趋势。子宫内膜上皮内瘤变（EIN）是一种癌前病变，若不及时治疗可能进展为EC。临床亟需一种模型，既能有效识别需要及时评估的女性，又能避免不必要的侵入性操作。肥胖是主要风险因素，但亚洲女性是否需要采用低于世界卫生组织（WHO）定义的体重指数（BMI）界值仍存争议。本研究旨在建立一个多变量风险预测模型，以指导活检决策，并确定适用于亚洲女性EIN/EC风险预测的BMI界值。 方法：本研究回顾性分析了2010年至2023年间在一家三级医院接受宫腔镜检查的1192名年龄≥18岁女性。候选预测因子包括患者年龄、产次、BMI、绝经状态、异常子宫出血（AUB）症状、糖尿病、高血压、多囊卵巢综合征（PCOS）、口服避孕药或宫内节育器使用情况、绝经激素治疗、他莫昔芬治疗、多发息肉情况以及经阴道超声测量的子宫内膜厚度（EMT）。通过逐步选择的多变量逻辑回归分析确定独立预测因子，并使用1000次Bootstrap重采样评估模型的稳定性和校准度。 结果：55名患者（4.6%）被诊断为EIN/EC。共识别出六个独立预测因子：绝经状态（调整后比值比[aOR] 5.93，95% CI 2.92–12.04）、AUB（aOR 4.07，1.51–10.97）、多发息肉（aOR 2.49，1.33–4.66）、PCOS（aOR 2.37，1.08–5.22）、BMI（每增加1 kg/m² aOR 1.13；每增加5 kg/m² aOR 1.84）以及EMT（每增加1 mm aOR 1.07，1.02–1.11）。当使用分类界值时，肥胖II级（BMI ≥ 30 kg/m²）和EMT显著增厚（≥20 mm）仍具有显著意义。预测概率范围从无风险因素时的0.3%到具备全部六个风险因素时的90.9%。最终模型显示出良好的区分度（AUC 0.79，95% CI 0.73–0.86）和在Bootstrap验证中优异的校准度（平均绝对误差0.005）。 结论：该六因素临床模型利用易获取的变量对个体EIN/EC风险进行分层，可通过识别高危患者指导基于风险的及时活检决策，同时最大限度减少低危病例的不必要操作。BMI ≥ 30 kg/m²（WHO肥胖阈值）被证实为有意义的界值，但需通过外部验证确认其普适性和临床适用性。
When Should We Biopsy? A Risk Factor-Based Predictive Model for EIN and Endometrial Cancer
肿瘤(癌症)患者之家